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Research Papers: Energy Systems Analysis

Assessment and Evolutionary Based Multi-Objective Optimization of a Novel Renewable-Based Polygeneration Energy System

[+] Author and Article Information
Rami S. El-Emam

Clean Energy Research Laboratory (CERL),
University of Ontario Institute
of Technology (UOIT),
Oshawa, ON L1H7K4, Canada;
Faculty of Engineering,
Mansoura University,
Mansoura 35516, Egypt
e-mail: rami.elemam@uoit.net

Ibrahim Dincer

Clean Energy Research Laboratory (CERL),
University of Ontario Institute
of Technology (UOIT),
Oshawa, ON L1H7K4, Canada
e-mail: Ibrahim.Dincer@uoit.ca

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received January 25, 2016; final manuscript received May 9, 2016; published online June 27, 2016. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 139(1), 012003 (Jun 27, 2016) (13 pages) Paper No: JERT-16-1050; doi: 10.1115/1.4033625 History: Received January 25, 2016; Revised May 09, 2016

In this paper, a renewable-based integrated energy system is developed, analyzed, and optimized to achieve better performance. The present system is designed to be driven by concentrated solar thermal and biomass energies. Biomass fuel is used as the backup source of energy when the solar energy is not available. The system is designed to produce electricity, cooling, and hydrogen. The power output of the system is provided by solar-driven regenerative helium gas turbine during day time and from biomass gasification driven solid oxide fuel cell (SOFC) unit at night time. The fuel cell stack number is estimated as to provide the same net power. The system operates at energy and exergy efficiencies of 39.99% and 27.47%, respectively, at the optimal point selected based on the optimization analysis. The parametric studies on performance and environmental impact assessment are performed to investigate the effects of several operating parameters on the system performance.

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Figures

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Fig. 1

Schematic diagram of the integrated energy system based on parabolic dish–gas turbine integrated systems with biomass gasification

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Fig. 2

Illustration of operation mode with respect to the variation in solar radiation density

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Fig. 3

Integrated system efficiency and greenhouse gas emissions

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Fig. 4

Exergy destruction in the main system components

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Fig. 5

(a) Effect of the solar receiver temperature on the dish and gas turbine exergy efficiency, (b) effect of the solar receiver temperature on system efficiency for 24 hrs performance, (c) total cost rate and emissions at different receiver temperature values for 24 hrs performance, and (d) efficiency of the integrated system during solar availability at different receiver temperature values during solar radiation availability (Solar On mode)

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Fig. 6

(a) Effect of the gas turbine compression ratio on the system performance for 24 hrs performance, (b) effect of the gas turbine compression ratio on the system performance during solar radiation availability (Solar On mode), and (c) effect of compression ratio of the gas turbine engine on the cost rate and the ECOPs for 24 hrs performance

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Fig. 7

(a) Effect of gasification temperature on the overall system performance for 24 hrs performance, (b) effect of gasification temperature on the system performance (Solar Off mode), and (c) efficiency of the integrated system during solar availability (Solar On mode) at different gasification temperature values

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Fig. 8

(a) Effect of concentration ratio of the solar dish on the exergy efficiency of the dish and (b) exergetic performance of the solar dish with concentration ratio

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Fig. 9

(a) Exergetic performance of the solar dish at different receiver temperature values and (b) solar dish exergy losses and exergy destruction items at different receiver temperature values

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Fig. 10

(a) Pareto frontier for the best trade-off values, (b) scattered distribution of the optimization decision variables, and (c) scattered distribution of the optimization decision variables

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